Field of Invention
The present invention relates to a multi-type BGA chip visual recognition method, and more particularly to a multi-type BGA chip visual recognition method using line-based-clustering approach.
Description of Related Arts
BGA (Ball Gray Array) packaging is currently widely used in integrated circuits due to its characteristics of high integration, large quantity of I/O solder balls, and excellent electronic properties. However, the denser I/O packaging and miniaturization are more likely to lead to chip defects such as ball missing, oversize or undersize balls, extra balls, misshapen balls, ball bridging, and ball offset during the manufacture process of BGA chips. In assembly processes of printed circuit boards using surface mount technology (SMT), the high density of I/O solder balls in BGA chips as well as the multiple alignment types of ball arrays have induced more stringent challenges in relation to accuracy and speed in identification and inspection.
At present, automated optical inspection (AOI) system is commonly applied to identify, inspect and locate the chips in SMT product processes, in which identification is the most basic. During the identification process, a standard parameter database is built for the particular type of chip in the AOI system by the defect-free chips, and this database will provide standard references for the inspection and positioning processes. Specifically for BGA cases, procedures in the AOI system include two stages, which are training (offline identification) and inspection (online inspection).
Training: Building a standard parameter database through identifying defect-free BGA chip samples. Standard database of BGA chip includes ball distribution matrix (distribution pattern of solder balls), ball standard area, ball standard roundness, ball standard row spacing, and ball standard column spacing. However, in the existing AOI systems, the standard parameters database is provided through manual measurements and manual entries. Thus the work load is great, the cost of manpower is high and the degree of automation is low.
Inspection: Extracting features form the BGA chip image and utilizing the standard database as references to diagnose the potential defects and locate the BGA position with respect to image coordinate frame. Common defects of BGA chip include the followings: ball missing, oversize or undersize ball, ball diameter or roundness failure, ball bridging and etc. The characteristics of BGA chips, which are large number of ball pins, small spacing and diverse form of ball alignments, have led to higher requirements in reliability and speed for the inspection algorithm.
The basic workflow in the inspection phase of BGA chip contains image acquisition, solder ball extraction, ball array positioning and ball feature detection. Wherein solder ball extraction is aimed at extracting BGA solder balls from the original acquired image through image segmentation algorithms. Conventional ball extraction methods usually employ a global threshold for image binarization: for example, in the literature “Analysis Ball Grid Array defects by using new image technique”, the mean and variance of all pixel gray values are utilized to calculate this global thresholding value; in the literature “A system for automated BGA Inspection”, Otsu's algorithm which is capable of adapting to different image brightness is used to compute the global threshold; in the literature “Automated detection and classification of non-wet solder joints”, a method of iterative computation based on the statistics of image pixel gray values is used to obtain this threshold.
Traditional methods based on the global threshold value will result in ‘over-segmentation’ or ‘under-segmentation’ balls, especially when BGA image are captured from the AOI system which has uneven brightness distribution, thus causing errors in detection results. At the same time, the noise introduced by the image segmentation may be mis-identified as a ball and hinder the subsequent process of ball grid array positioning.
Ball grid array positioning is aimed at determining the position and orientation angle of the BGA with respect to image coordinate system. The mature existing methods (such as HALCON machine vision software) usually employ template matching strategy. Specifically for implementations, a standard ball grid array template is constructed based on BGA information from the standard database, then iterative ball traversals using the relative location relationship of adjacent balls are performed to determine the spatial transformation between BGA template and BGA chip for testing. Since a great volume of traversals and iterations are involved in this type of methods, the calculation complexity and execution time will surge as the number of BGA solder ball rises. Therefore these methods are very difficult to apply to the practical AOI system with high real-time requirements. Also, since the inspection requires the building of a standard BGA template, the algorithm flexibility is decreased. In order to reduce the time complexity in BGA positioning process, literature ‘locating and checking of BGA pins position using gray level’ adopted a rectangular least squares method to trace the outer border balls for obtaining rotation angle of BGA. However, this method is developed based on the assumption that border balls are strictly aligned as a stand rectangular. Otherwise, the minimum rectangle enclosing the outliers would result in failure. In the literature ‘A system for automated BGA Inspection’, for the purpose of avoiding processing template matching for all balls, a solder ball located at a particular position of ball array (such as the corner of ball array) is selected to align the inspected BGA to the template. However, this method also fails to address the inconvenience of building a standard template and to provide robustness to the non-ball interferences which are introduced by segmentation stage.
Accordingly, the problems of existing AOI system in relation to identification and inspection process of BGA chip are summarized as follows:
1) In the offline identification process, manual measurements of BGA sample parameters and manual entry of standard parameter data are required for establishing the standard database. The workload is great and the cost of manpower is high.
2) Traditional methods utilize one global threshold value to perform ball segmentation. Consequently, the robustness to image with uneven light distributions is poor and under-segmentation or over-segmentation is easily introduced, thus causing inspection errors.
3) The BGA positioning algorithm based on rectangular least squires or a particular reference ball can only apply to a limited number of BGAs with specified ball arrangements. The applicability to BGAs with sparsely arranged balls or with irregular ball distributions is poor.
4) The BGA positioning algorithm based on traditional template matching method requires the building of standard array template for each type of BGA chips, furthermore, the time complexity for matching is high and the robustness to interferences introduced by ball segmentation process is decreased.